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Record W2912183271 · doi:10.1080/19407963.2019.1569433

Reflections on major sport event volunteer legacy research

2019· article· en· W2912183271 on OpenAlexaffabout
Alison Doherty, Swarali Patil

Bibliographic record

VenueJournal of Policy Research in Tourism Leisure and Events · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsEvent (particle physics)VolunteerHistorySociologyBiology

Abstract

fetched live from OpenAlex

Despite the varying attention over time to economic, infrastructure, tourism, political, environmental, social, and health aspects of the legacy of hosting a major sport eventand acknowledging that this may not be an exhaustive listthe human factor persists.This is not surprising given the acknowledged critical roles and vast numbers of volunteers engaged in the hosting of major sport events like the Olympics or FIFA World Cup (IOC, 2013;Nichols & Ralston, 2011;Zhuang & Girginov, 2012).Communities of all sizes across the world host what may be considered, from their per-spective, major sport events. 1 The volunteer workforce is just one investment of the host community in staging an outstanding if not 'world class' event.Certainly, it is critical to ensure the 'heroes of these Games' (IOC, 2012) are effective.However, there is a growing focus on the legacy -'the tangible and intangible structures created for and by a sport event that remain longer than the event itself' (Preuss, 2007, p. 211)of that volunteer effort and engagement (DCMS, 2012;IOC, 2008).The 'carryover effect of ongoing com-munity support' (Doherty, 2009, p. 187) as a legacy may be represented by continued or increased volunteer support for other community special events and enhanced community volunteerism in general, in terms of positive attitudes towards, and increased rates and levels of, volunteering.To that end, scholars continue to investigate the volunteer legacy of major sport events in order to understand the nature of such an effect, as well as its causes and mechanisms, with implications for event bidding and hosting policy, planning, and execution.Doherty's study of the Jeux du Canada Games volunteers, published in this journal in 2009, is but one investigation along this path.At the time of its publication, only a few scholars had examined future volunteering intentions of major sport event volunteers, and almost all of that work was descriptive in nature (Doherty, 2009).Doherty relied on social exchange theory to frame an examination of the impact of the perceived costs and benefits of Games volunteering on future intentions of planning and on-site or Games delivery volunteers.In

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.205
GPT teacher head0.558
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2019
Admission routes2
Has abstractyes

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